Ling Pan (潘玲)


Postdoctoral Fellow
MILA (Montreal Institute for Learning Algorithms)

(Incoming) Assistant Professor
Department of Electronic and Computer Engineering
Hong Kong University of Science and Technology (HKUST)

Email: penny.ling.pan [@] gmail [DOT] com

About Me

I will be joining the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology (HKUST) as a Tenure-Track Assistant Professor in Spring 2024.

I am a postdoctoral fellow at MILA supervised by Prof. Yoshua Bengio. Prior to that, I received my Ph.D. from the Institute for Interdisciplinary Information Sciences (IIIS) (headed by Prof. Andrew Yao), Tsinghua University in 2022, advised by Prof. Longbo Huang. I received my B.E. from the School of Computer Science and Engineering, Sun Yat-Sen (Zhongshan) University, Guangzhou, China in 2017.

During my Ph.D., I was fortunate to visit Stanford University advised by Prof. Tengyu Ma, University of Oxford advised by Prof. Shimon Whiteson, and I was a research intern in the Machine Learning Group at Microsoft Research Asia advised by Dr. Wei Chen. I was also a recepient of Microsoft Research Ph.D. Fellowship (Asia) (2020).

Please drop me an email if you are interested in collaborating with me.

Prospective Students: I am actively looking for self-motivated students (including undergraduate/graduate students and research assistants) who are interested in the areas of artificial intelligence, machine learning, deep reinforcement learning, generative flow networks, and multi-agent systems. I have several PhD/MPhil/RA openings starting in Spring/Fall 2024 at HKUST. Please drop me an email with your CV if you are interested.

Research Interests

My research interests mainly include theoretical understanding, algorithmic improvements and practical application of generative flow networks (GFlowNets), reinforcement learning and multi-agent systems. I focus on developing robust, efficient, and practical deep reinforcement learning algorithms. I am also interested in the application of reinforcement learning in practical problems like computational sustainability and drug discovery.


(* indicates equal contribution)


Selected Awards

Professional Activities

Selected Talks/Presentations